Underwater Acoustic Target Recognition Techniques

Underwater acoustic target recognition employs passive sonar to classify and identify objects from ship-radiated or ambient noise. The field has evolved from classical signal-processing pipelines—based on spectral analysis, wavelet and Hilbert-Huang transforms, and Mel-frequency cepstral coefficients—paired with conventional classifiers such as support-vector machines and extreme learning machines, to end-to-end deep-learning frameworks. Contemporary methods exploit raw waveform or time–frequency representations with convolutional neural networks, deep belief networks and residual architectures. They address challenges of variable propagation paths, reverberation, low signal-to-noise ratios and limited labelled data through multiscale filter designs, attention mechanisms and data-augmentation strategies. Applications span naval surveillance, marine biodiversity monitoring and infrastructure inspection, reflecting a convergence of acoustics, machine learning and ocean engineering.

Research from Nature Portfolio

Recent studies have introduced multiscale residual units to build deep convolution stacks that operate on raw acoustic waveforms. By replacing large initial kernels with cascaded multiscale residual blocks, these architectures achieve balanced depth and enhanced nonlinear representation. Validation on real-world datasets demonstrated improved intra-class compactness and inter-class separation, boosting recognition accuracy by nearly seven percentage points over conventional waveform-based networks and four points compared to time–frequency models.

Underwater Acoustic Target Recognition Techniques publication trend

The graph below shows the total number of articles in underwater acoustic target recognition techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Mel-frequency cepstral coefficients (MFCC): Spectral features that represent the short-term power spectrum of a sound using a mel-scale filter bank.

Convolutional neural network (CNN): A deep-learning architecture that applies spatially local filters to extract hierarchical features from input data.

Depthwise separable convolution: A factorised convolution that splits spatial and channel filtering to reduce computation while preserving feature extraction.

Residual network: A deep architecture that uses skip connections to alleviate vanishing gradients and enable training of very deep models.

References

1. Deep convolution stack for waveform in underwater acoustic target recognition. Scientific Reports (2021).
2. Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks. Sensors (2021).
3. Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network. Remote Sensing (2019).